Road surface condition guided decision making and prediction

ABSTRACT

Among other things, techniques are described for receiving, from at least one sensor of a vehicle, sensor data associated with a surface along a path to be traveled by a vehicle; using a surface classifier to determine a classification of the surface based on the sensor data; determining, based on the classification of the surface, drivability properties of the surface; planning, based on the drivability properties of the surface, a behavior of the vehicle when driving near the surface or on the surface; and controlling the vehicle based on the planned behavior.

FIELD OF THE INVENTION

This description relates to road surface condition guided decisionmaking and prediction.

BACKGROUND

Surfaces on which vehicles drive can vary along vehicle paths. Forexample, road surfaces along a vehicle path can include asphalt,concrete, rock, etc. These surfaces can also dynamically change underdifferent conditions, such as weather conditions (e.g., rain, snow,sleet, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of an autonomous vehicle having autonomouscapability.

FIG. 2 shows an example “cloud” computing environment.

FIG. 3 shows a computer system.

FIG. 4 shows an example architecture for an autonomous vehicle.

FIG. 5 shows an example of inputs and outputs that can be used by aperception module.

FIG. 6 shows an example of a LiDAR system.

FIG. 7 shows the LiDAR system in operation.

FIG. 8 shows the operation of the LiDAR system in additional detail.

FIG. 9 shows a block diagram of the relationships between inputs andoutputs of a planning module.

FIG. 10 shows a directed graph used in path planning.

FIG. 11 shows a block diagram of the inputs and outputs of a controlmodule.

FIG. 12 shows a block diagram of the inputs, outputs, and components ofa controller.

FIG. 13A, FIG. 13B, and FIG. 13C show block diagrams of example systemsfor surface guided decision making.

FIG. 14 shows a flowchart of an example method.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

In the drawings, specific arrangements or orderings of schematicelements, such as those representing devices, modules, instructionblocks, and data elements, are shown for ease of description. However,it should be understood by those skilled in the art that the specificordering or arrangement of the schematic elements in the drawings is notmeant to imply that a particular order or sequence of processing, orseparation of processes, is required. Further, the inclusion of aschematic element in a drawing is not meant to imply that such elementis required in all embodiments or that the features represented by suchelement may not be included in or combined with other elements in someembodiments.

Further, in the drawings, where connecting elements, such as solid ordashed lines or arrows, are used to illustrate a connection,relationship, or association between or among two or more otherschematic elements, the absence of any such connecting elements is notmeant to imply that no connection, relationship, or association canexist. In other words, some connections, relationships, or associationsbetween elements are not shown in the drawings so as not to obscure thedisclosure. In addition, for ease of illustration, a single connectingelement is used to represent multiple connections, relationships, orassociations between elements. For example, where a connecting elementrepresents a communication of signals, data, or instructions, it shouldbe understood by those skilled in the art that such element representsone or multiple signal paths (e.g., a bus), as may be needed, to affectthe communication.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments may be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

Several features are described hereafter that can each be usedindependently of one another or with any combination of other features.However, any individual feature may not address any of the problemsdiscussed above or might only address one of the problems discussedabove. Some of the problems discussed above might not be fully addressedby any of the features described herein. Although headings are provided,information related to a particular heading, but not found in thesection having that heading, may also be found elsewhere in thisdescription. Embodiments are described herein according to the followingoutline:

-   -   1. General Overview    -   2. System Overview    -   3. Autonomous Vehicle Architecture    -   4. Autonomous Vehicle Inputs    -   5. Autonomous Vehicle Planning    -   6. Autonomous Vehicle Control    -   7. Surface Guided Decision Making

General Overview

Behavior of a vehicle is adapted based on dynamically changing roadsurfaces and conditions that impact safety and drivability. For example,sensor measurements are used to identify and categorize road surfaces.Based on the road surface category, the vehicle can determine thedrivability properties of the surface, and can make appropriate planningdecisions. Additionally, based on the drivability properties of thesurface, the vehicle can predict the behavior of other vehicles that aredriving on the surface and can proactively adjust its behavioraccordingly. In this way, the vehicle can exhibit behaviors similar tothat of a human driver in hazardous conditions, such as followingexisting tracks on the road when it is snowing or raining, avoiding icepatches, reducing speed, biasing within lanes, or changing lanes toavoid an obstacle on the road.

Adapting the behavior of a vehicle based on the dynamically changingroad surfaces and conditions improves the safety and reliability of thevehicle, particularly when driving in hazardous environments.Additionally, recognizing that the behavior of other vehicles changesbased on the dynamically changing road surfaces and conditions improvesthe accuracy of predicting the behavior of the other vehicles. This, inturn, reduces the chances of collisions and improves vehicle reliabilityand safety.

System Overview

FIG. 1 shows an example of an autonomous vehicle 100 having autonomouscapability.

As used herein, the term “autonomous capability” refers to a function,feature, or facility that enables a vehicle to be partially or fullyoperated without real-time human intervention, including withoutlimitation fully autonomous vehicles, highly autonomous vehicles, andconditionally autonomous vehicles.

As used herein, an autonomous vehicle (AV) is a vehicle that possessesautonomous capability.

As used herein, “vehicle” includes means of transportation of goods orpeople. For example, cars, buses, trains, airplanes, drones, trucks,boats, ships, submersibles, dirigibles, etc. A driverless car is anexample of a vehicle.

As used herein, “trajectory” refers to a path or route to navigate an AVfrom a first spatiotemporal location to second spatiotemporal location.In an embodiment, the first spatiotemporal location is referred to asthe initial or starting location and the second spatiotemporal locationis referred to as the destination, final location, goal, goal position,or goal location. In some examples, a trajectory is made up of one ormore segments (e.g., sections of road) and each segment is made up ofone or more blocks (e.g., portions of a lane or intersection). In anembodiment, the spatiotemporal locations correspond to real worldlocations. For example, the spatiotemporal locations are pick up ordrop-off locations to pick up or drop-off persons or goods.

As used herein, “sensor(s)” includes one or more hardware componentsthat detect information about the environment surrounding the sensor.Some of the hardware components can include sensing components (e.g.,image sensors, biometric sensors), transmitting and/or receivingcomponents (e.g., laser or radio frequency wave transmitters andreceivers), electronic components such as analog-to-digital converters,a data storage device (such as a RAM and/or a nonvolatile storage),software or firmware components and data processing components such asan ASIC (application-specific integrated circuit), a microprocessorand/or a microcontroller.

As used herein, a “scene description” is a data structure (e.g., list)or data stream that includes one or more classified or labeled objectsdetected by one or more sensors on the AV vehicle or provided by asource external to the AV.

As used herein, a “road” is a physical area that can be traversed by avehicle, and may correspond to a named thoroughfare (e.g., city street,interstate freeway, etc.) or may correspond to an unnamed thoroughfare(e.g., a driveway in a house or office building, a section of a parkinglot, a section of a vacant lot, a dirt path in a rural area, etc.).Because some vehicles (e.g., 4-wheel-drive pickup trucks, sport utilityvehicles, etc.) are capable of traversing a variety of physical areasnot specifically adapted for vehicle travel, a “road” may be a physicalarea not formally defined as a thoroughfare by any municipality or othergovernmental or administrative body.

As used herein, a “lane” is a portion of a road that can be traversed bya vehicle. A lane is sometimes identified based on lane markings. Forexample, a lane may correspond to most or all of the space between lanemarkings, or may correspond to only some (e.g., less than 50%) of thespace between lane markings. For example, a road having lane markingsspaced far apart might accommodate two or more vehicles between themarkings, such that one vehicle can pass the other without traversingthe lane markings, and thus could be interpreted as having a lanenarrower than the space between the lane markings, or having two lanesbetween the lane markings. A lane could also be interpreted in theabsence of lane markings. For example, a lane may be defined based onphysical features of an environment, e.g., rocks and trees along athoroughfare in a rural area or, e.g., natural obstructions to beavoided in an undeveloped area. A lane could also be interpretedindependent of lane markings or physical features. For example, a lanecould be interpreted based on an arbitrary path free of obstructions inan area that otherwise lacks features that would be interpreted as laneboundaries. In an example scenario, an AV could interpret a lane throughan obstruction-free portion of a field or empty lot. In another examplescenario, an AV could interpret a lane through a wide (e.g., wide enoughfor two or more lanes) road that does not have lane markings. In thisscenario, the AV could communicate information about the lane to otherAVs so that the other AVs can use the same lane information tocoordinate path planning among themselves.

The term “over-the-air (OTA) client” includes any AV, or any electronicdevice (e.g., computer, controller, IoT device, electronic control unit(ECU)) that is embedded in, coupled to, or in communication with an AV.

The term “over-the-air (OTA) update” means any update, change, deletionor addition to software, firmware, data or configuration settings, orany combination thereof, that is delivered to an OTA client usingproprietary and/or standardized wireless communications technology,including but not limited to: cellular mobile communications (e.g., 2G,3G, 4G, 5G), radio wireless area networks (e.g., WiFi) and/or satelliteInternet.

The term “edge node” means one or more edge devices coupled to a networkthat provide a portal for communication with AVs and can communicatewith other edge nodes and a cloud based computing platform, forscheduling and delivering OTA updates to OTA clients.

The term “edge device” means a device that implements an edge node andprovides a physical wireless access point (AP) into enterprise orservice provider (e.g., VERIZON, AT&T) core networks. Examples of edgedevices include but are not limited to: computers, controllers,transmitters, routers, routing switches, integrated access devices(IADs), multiplexers, metropolitan area network (MAN) and wide areanetwork (WAN) access devices.

“One or more” includes a function being performed by one element, afunction being performed by more than one element, e.g., in adistributed fashion, several functions being performed by one element,several functions being performed by several elements, or anycombination of the above.

It will also be understood that, although the terms first, second, etc.are, in some instances, used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. For example, a first contactcould be termed a second contact, and, similarly, a second contact couldbe termed a first contact, without departing from the scope of thevarious described embodiments. The first contact and the second contactare both contacts, but they are not the same contact.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used in this description, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when”or “upon” or “in response to determining” or “in response to detecting,”depending on the context. Similarly, the phrase “if it is determined” or“if [a stated condition or event] is detected” is, optionally, construedto mean “upon determining” or “in response to determining” or “upondetecting [the stated condition or event]” or “in response to detecting[the stated condition or event],” depending on the context.

As used herein, an AV system refers to the AV along with the array ofhardware, software, stored data, and data generated in real-time thatsupports the operation of the AV. In an embodiment, the AV system isincorporated within the AV. In an embodiment, the AV system is spreadacross several locations. For example, some of the software of the AVsystem is implemented on a cloud computing environment similar to cloudcomputing environment 200 described below with respect to FIG. 2.

In general, this document describes technologies applicable to anyvehicles that have one or more autonomous capabilities including fullyautonomous vehicles, highly autonomous vehicles, and conditionallyautonomous vehicles, such as so-called Level 5, Level 4 and Level 3vehicles, respectively (see SAE International's standard J3016: Taxonomyand Definitions for Terms Related to On-Road Motor Vehicle AutomatedDriving Systems, which is incorporated by reference in its entirety, formore details on the classification of levels of autonomy in vehicles).The technologies described in this document are also applicable topartially autonomous vehicles and driver assisted vehicles, such asso-called Level 2 and Level 1 vehicles (see SAE International's standardJ3016: Taxonomy and Definitions for Terms Related to On-Road MotorVehicle Automated Driving Systems). In an embodiment, one or more of theLevel 1, 2, 3, 4 and 5 vehicle systems may automate certain vehicleoperations (e.g., steering, braking, and using maps) under certainoperating conditions based on processing of sensor inputs. Thetechnologies described in this document can benefit vehicles in anylevels, ranging from fully autonomous vehicles to human-operatedvehicles.

Autonomous vehicles have advantages over vehicles that require a humandriver. One advantage is safety. For example, in 2016, the United Statesexperienced 6 million automobile accidents, 2.4 million injuries, 40,000fatalities, and 13 million vehicles in crashes, estimated at a societalcost of $910+ billion. U.S. traffic fatalities per 100 million milestraveled have been reduced from about six to about one from 1965 to2015, in part due to additional safety measures deployed in vehicles.For example, an additional half second of warning that a crash is aboutto occur is believed to mitigate 60% of front-to-rear crashes. However,passive safety features (e.g., seat belts, airbags) have likely reachedtheir limit in improving this number. Thus, active safety measures, suchas automated control of a vehicle, are the likely next step in improvingthese statistics. Because human drivers are believed to be responsiblefor a critical pre-crash event in 95% of crashes, automated drivingsystems are likely to achieve better safety outcomes, e.g., by reliablyrecognizing and avoiding critical situations better than humans; makingbetter decisions, obeying traffic laws, and predicting future eventsbetter than humans; and reliably controlling a vehicle better than ahuman.

Referring to FIG. 1, an AV system 120 operates the vehicle 100 along atrajectory 198 through an environment 190 to a destination 199(sometimes referred to as a final location) while avoiding objects(e.g., natural obstructions 191, vehicles 193, pedestrians 192,cyclists, and other obstacles) and obeying rules of the road (e.g.,rules of operation or driving preferences).

In an embodiment, the AV system 120 includes devices 101 that areinstrumented to receive and act on operational commands from thecomputer processors 146. We use the term “operational command” to meanan executable instruction (or set of instructions) that causes a vehicleto perform an action (e.g., a driving maneuver). Operational commandscan, without limitation, including instructions for a vehicle to startmoving forward, stop moving forward, start moving backward, stop movingbackward, accelerate, decelerate, perform a left turn, and perform aright turn. In an embodiment, computing processors 146 are similar tothe processor 304 described below in reference to FIG. 3. Examples ofdevices 101 include a steering control 102, brakes 103, gears,accelerator pedal or other acceleration control mechanisms, windshieldwipers, side-door locks, window controls, and turn-indicators.

In an embodiment, the AV system 120 includes sensors 121 for measuringor inferring properties of state or condition of the vehicle 100, suchas the AV's position, linear and angular velocity and acceleration, andheading (e.g., an orientation of the leading end of vehicle 100).Example of sensors 121 are GPS, inertial measurement units (IMU) thatmeasure both vehicle linear accelerations and angular rates, wheel speedsensors for measuring or estimating wheel slip ratios, wheel brakepressure or braking torque sensors, engine torque or wheel torquesensors, and steering angle and angular rate sensors.

In an embodiment, the sensors 121 also include sensors for sensing ormeasuring properties of the AV's environment. For example, monocular orstereo video cameras 122 in the visible light, infrared or thermal (orboth) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight(TOF) depth sensors, speed sensors, temperature sensors, humiditysensors, and precipitation sensors.

In an embodiment, the AV system 120 includes a data storage unit 142 andmemory 144 for storing machine instructions associated with computerprocessors 146 or data collected by sensors 121. In an embodiment, thedata storage unit 142 is similar to the ROM 308 or storage device 310described below in relation to FIG. 3. In an embodiment, memory 144 issimilar to the main memory 306 described below. In an embodiment, thedata storage unit 142 and memory 144 store historical, real-time, and/orpredictive information about the environment 190. In an embodiment, thestored information includes maps, driving performance, trafficcongestion updates or weather conditions. In an embodiment, datarelating to the environment 190 is transmitted to the vehicle 100 via acommunications channel from a remotely located database 134.

In an embodiment, the AV system 120 includes communications devices 140for communicating measured or inferred properties of other vehicles'states and conditions, such as positions, linear and angular velocities,linear and angular accelerations, and linear and angular headings to thevehicle 100. These devices include Vehicle-to-Vehicle (V2V) andVehicle-to-Infrastructure (V2I) communication devices and devices forwireless communications over point-to-point or ad hoc networks or both.In an embodiment, the communications devices 140 communicate across theelectromagnetic spectrum (including radio and optical communications) orother media (e.g., air and acoustic media). A combination ofVehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication(and, in some embodiments, one or more other types of communication) issometimes referred to as Vehicle-to-Everything (V2X) communication. V2Xcommunication typically conforms to one or more communications standardsfor communication with, between, and among autonomous vehicles.

In an embodiment, the communication devices 140 include communicationinterfaces. For example, wired, wireless, WiMAX, Wi-Fi, Bluetooth,satellite, cellular, optical, near field, infrared, or radio interfaces.The communication interfaces transmit data from a remotely locateddatabase 134 to AV system 120. In an embodiment, the remotely locateddatabase 134 is embedded in a cloud computing environment 200 asdescribed in FIG. 2. The communication devices 140 transmit datacollected from sensors 121 or other data related to the operation ofvehicle 100 to the remotely located database 134. In an embodiment,communication devices 140 transmit information that relates toteleoperations to the vehicle 100. In some embodiments, the vehicle 100communicates with other remote (e.g., “cloud”) servers 136.

In an embodiment, the remotely located database 134 also stores andtransmits digital data (e.g., storing data such as road and streetlocations). Such data is stored on the memory 144 on the vehicle 100, ortransmitted to the vehicle 100 via a communications channel from theremotely located database 134.

In an embodiment, the remotely located database 134 stores and transmitshistorical information about driving properties (e.g., speed andacceleration profiles) of vehicles that have previously traveled alongtrajectory 198 at similar times of day. In one implementation, such datacan be stored on the memory 144 on the vehicle 100, or transmitted tothe vehicle 100 via a communications channel from the remotely locateddatabase 134.

Computer processors 146 located on the vehicle 100 algorithmicallygenerate control actions based on both real-time sensor data and priorinformation, allowing the AV system 120 to execute its autonomousdriving capabilities.

In an embodiment, the AV system 120 includes computer peripherals 132coupled to computer processors 146 for providing information and alertsto, and receiving input from, a user (e.g., an occupant or a remoteuser) of the vehicle 100. In an embodiment, peripherals 132 are similarto the display 312, input device 314, and cursor controller 316discussed below in reference to FIG. 3. The coupling is wireless orwired. Any two or more of the interface devices can be integrated into asingle device.

In an embodiment, the AV system 120 receives and enforces a privacylevel of a passenger, e.g., specified by the passenger or stored in aprofile associated with the passenger. The privacy level of thepassenger determines how particular information associated with thepassenger (e.g., passenger comfort data, biometric data, etc.) ispermitted to be used, stored in the passenger profile, and/or stored onthe cloud server 136 and associated with the passenger profile. In anembodiment, the privacy level specifies particular informationassociated with a passenger that is deleted once the ride is completed.In an embodiment, the privacy level specifies particular informationassociated with a passenger and identifies one or more entities that areauthorized to access the information. Examples of specified entitiesthat are authorized to access information can include other AVs, thirdparty AV systems, or any entity that could potentially access theinformation.

A privacy level of a passenger can be specified at one or more levels ofgranularity. In an embodiment, a privacy level identifies specificinformation to be stored or shared. In an embodiment, the privacy levelapplies to all the information associated with the passenger such thatthe passenger can specify that none of her personal information isstored or shared. Specification of the entities that are permitted toaccess particular information can also be specified at various levels ofgranularity. Various sets of entities that are permitted to accessparticular information can include, for example, other AVs, cloudservers 136, specific third party AV systems, etc.

In an embodiment, the AV system 120 or the cloud server 136 determinesif certain information associated with a passenger can be accessed bythe AV 100 or another entity. For example, a third-party AV system thatattempts to access passenger input related to a particularspatiotemporal location must obtain authorization, e.g., from the AVsystem 120 or the cloud server 136, to access the information associatedwith the passenger. For example, the AV system 120 uses the passenger'sspecified privacy level to determine whether the passenger input relatedto the spatiotemporal location can be presented to the third-party AVsystem, the AV 100, or to another AV. This enables the passenger'sprivacy level to specify which other entities are allowed to receivedata about the passenger's actions or other data associated with thepassenger.

FIG. 2 shows an example “cloud” computing environment. Cloud computingis a model of service delivery for enabling convenient, on-demandnetwork access to a shared pool of configurable computing resources(e.g. networks, network bandwidth, servers, processing, memory, storage,applications, virtual machines, and services). In typical cloudcomputing systems, one or more large cloud data centers house themachines used to deliver the services provided by the cloud. Referringnow to FIG. 2, the cloud computing environment 200 includes cloud datacenters 204 a, 204 b, and 204 c that are interconnected through thecloud 202. Data centers 204 a, 204 b, and 204 c provide cloud computingservices to computer systems 206 a, 206 b, 206 c, 206 d, 206 e, and 206f connected to cloud 202.

The cloud computing environment 200 includes one or more cloud datacenters. In general, a cloud data center, for example the cloud datacenter 204 a shown in FIG. 2, refers to the physical arrangement ofservers that make up a cloud, for example the cloud 202 shown in FIG. 2,or a particular portion of a cloud. For example, servers are physicallyarranged in the cloud datacenter into rooms, groups, rows, and racks. Acloud datacenter has one or more zones, which include one or more roomsof servers. Each room has one or more rows of servers, and each rowincludes one or more racks. Each rack includes one or more individualserver nodes. In some implementation, servers in zones, rooms, racks,and/or rows are arranged into groups based on physical infrastructurerequirements of the datacenter facility, which include power, energy,thermal, heat, and/or other requirements. In an embodiment, the servernodes are similar to the computer system described in FIG. 3. The datacenter 204 a has many computing systems distributed through many racks.

The cloud 202 includes cloud data centers 204 a, 204 b, and 204 c alongwith the network and networking resources (for example, networkingequipment, nodes, routers, switches, and networking cables) thatinterconnect the cloud data centers 204 a, 204 b, and 204 c and helpfacilitate the computing systems' 206 a-f access to cloud computingservices. In an embodiment, the network represents any combination ofone or more local networks, wide area networks, or internetworks coupledusing wired or wireless links deployed using terrestrial or satelliteconnections. Data exchanged over the network, is transferred using anynumber of network layer protocols, such as Internet Protocol (IP),Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM),Frame Relay, etc. Furthermore, in embodiments where the networkrepresents a combination of multiple sub-networks, different networklayer protocols are used at each of the underlying sub-networks. In someembodiments, the network represents one or more interconnectedinternetworks, such as the public Internet.

The computing systems 206 a-f or cloud computing services consumers areconnected to the cloud 202 through network links and network adapters.In an embodiment, the computing systems 206 a-f are implemented asvarious computing devices, for example servers, desktops, laptops,tablet, smartphones, Internet of Things (IoT) devices, autonomousvehicles (including, cars, drones, shuttles, trains, buses, etc.) andconsumer electronics. In an embodiment, the computing systems 206 a-fare implemented in or as a part of other systems.

FIG. 3 shows a computer system 300. In an implementation, the computersystem 300 is a special purpose computing device. The special-purposecomputing device is hard-wired to perform the techniques or includesdigital electronic devices such as one or more application-specificintegrated circuits (ASICs) or field programmable gate arrays (FPGAs)that are persistently programmed to perform the techniques, or caninclude one or more general purpose hardware processors programmed toperform the techniques pursuant to program instructions in firmware,memory, other storage, or a combination. Such special-purpose computingdevices can also combine custom hard-wired logic, ASICs, or FPGAs withcustom programming to accomplish the techniques. In various embodiments,the special-purpose computing devices are desktop computer systems,portable computer systems, handheld devices, network devices or anyother device that incorporates hard-wired and/or program logic toimplement the techniques.

In an embodiment, the computer system 300 includes a bus 302 or othercommunication mechanism for communicating information, and a processor304 coupled with a bus 302 for processing information. The processor 304is, for example, a general-purpose microprocessor. The computer system300 also includes a main memory 306, such as a random-access memory(RAM) or other dynamic storage device, coupled to the bus 302 forstoring information and instructions to be executed by processor 304. Inone implementation, the main memory 306 is used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by the processor 304. Such instructions,when stored in non-transitory storage media accessible to the processor304, render the computer system 300 into a special-purpose machine thatis customized to perform the operations specified in the instructions.

In an embodiment, the computer system 300 further includes a read onlymemory (ROM) 308 or other static storage device coupled to the bus 302for storing static information and instructions for the processor 304. Astorage device 310, such as a magnetic disk, optical disk, solid-statedrive, or three-dimensional cross point memory is provided and coupledto the bus 302 for storing information and instructions.

In an embodiment, the computer system 300 is coupled via the bus 302 toa display 312, such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), plasma display, light emitting diode (LED) display, or anorganic light emitting diode (OLED) display for displaying informationto a computer user. An input device 314, including alphanumeric andother keys, is coupled to bus 302 for communicating information andcommand selections to the processor 304. Another type of user inputdevice is a cursor controller 316, such as a mouse, a trackball, atouch-enabled display, or cursor direction keys for communicatingdirection information and command selections to the processor 304 andfor controlling cursor movement on the display 312. This input devicetypically has two degrees of freedom in two axes, a first axis (e.g.,x-axis) and a second axis (e.g., y-axis), that allows the device tospecify positions in a plane.

According to one embodiment, the techniques herein are performed by thecomputer system 300 in response to the processor 304 executing one ormore sequences of one or more instructions contained in the main memory306. Such instructions are read into the main memory 306 from anotherstorage medium, such as the storage device 310. Execution of thesequences of instructions contained in the main memory 306 causes theprocessor 304 to perform the process steps described herein. Inalternative embodiments, hard-wired circuitry is used in place of or incombination with software instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media includes non-volatilemedia and/or volatile media. Non-volatile media includes, for example,optical disks, magnetic disks, solid-state drives, or three-dimensionalcross point memory, such as the storage device 310. Volatile mediaincludes dynamic memory, such as the main memory 306. Common forms ofstorage media include, for example, a floppy disk, a flexible disk, harddisk, solid-state drive, magnetic tape, or any other magnetic datastorage medium, a CD-ROM, any other optical data storage medium, anyphysical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NV-RAM, or any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise the bus 302. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infrared data communications.

In an embodiment, various forms of media are involved in carrying one ormore sequences of one or more instructions to the processor 304 forexecution. For example, the instructions are initially carried on amagnetic disk or solid-state drive of a remote computer. The remotecomputer loads the instructions into its dynamic memory and send theinstructions over a telephone line using a modem. A modem local to thecomputer system 300 receives the data on the telephone line and use aninfrared transmitter to convert the data to an infrared signal. Aninfrared detector receives the data carried in the infrared signal andappropriate circuitry places the data on the bus 302. The bus 302carries the data to the main memory 306, from which processor 304retrieves and executes the instructions. The instructions received bythe main memory 306 can optionally be stored on the storage device 310either before or after execution by processor 304.

The computer system 300 also includes a communication interface 318coupled to the bus 302. The communication interface 318 provides atwo-way data communication coupling to a network link 320 that isconnected to a local network 322. For example, the communicationinterface 318 is an integrated service digital network (ISDN) card,cable modem, satellite modem, or a modem to provide a data communicationconnection to a corresponding type of telephone line. As anotherexample, the communication interface 318 is a local area network (LAN)card to provide a data communication connection to a compatible LAN. Insome implementations, wireless links are also implemented. In any suchimplementation, the communication interface 318 sends and receiveselectrical, electromagnetic, or optical signals that carry digital datastreams representing various types of information.

The network link 320 typically provides data communication through oneor more networks to other data devices. For example, the network link320 provides a connection through the local network 322 to a hostcomputer 324 or to a cloud data center or equipment operated by anInternet Service Provider (ISP) 326. The ISP 326 in turn provides datacommunication services through the world-wide packet data communicationnetwork now commonly referred to as the “Internet” 328. The localnetwork 322 and Internet 328 both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on the network link 320 and through thecommunication interface 318, which carry the digital data to and fromthe computer system 300, are example forms of transmission media. In anembodiment, the network 320 contains the cloud 202 or a part of thecloud 202 described above.

The computer system 300 sends messages and receives data, includingprogram code, through the network(s), the network link 320, and thecommunication interface 318. In an embodiment, the computer system 300receives code for processing. The received code is executed by theprocessor 304 as it is received, and/or stored in storage device 310, orother non-volatile storage for later execution.

Autonomous Vehicle Architecture

FIG. 4 shows an example architecture 400 for an autonomous vehicle(e.g., the vehicle 100 shown in FIG. 1). The architecture 400 includes aperception module 402 (sometimes referred to as a perception circuit), aplanning module 404 (sometimes referred to as a planning circuit), acontrol module 406 (sometimes referred to as a control circuit), alocalization module 408 (sometimes referred to as a localizationcircuit), and a database module 410 (sometimes referred to as a databasecircuit). Each module plays a role in the operation of the vehicle 100.Together, the modules 402, 404, 406, 408, and 410 can be part of the AVsystem 120 shown in FIG. 1. In some embodiments, any of the modules 402,404, 406, 408, and 410 is a combination of computer software (e.g.,executable code stored on a computer-readable medium) and computerhardware (e.g., one or more microprocessors, microcontrollers,application-specific integrated circuits [ASICs]), hardware memorydevices, other types of integrated circuits, other types of computerhardware, or a combination of any or all of these things). Each of themodules 402, 404, 406, 408, and 410 is sometimes referred to as aprocessing circuit (e.g., computer hardware, computer software, or acombination of the two). A combination of any or all of the modules 402,404, 406, 408, and 410 is also an example of a processing circuit.

In use, the planning module 404 receives data representing a destination412 and determines data representing a trajectory 414 (sometimesreferred to as a route) that can be traveled by the vehicle 100 to reach(e.g., arrive at) the destination 412. In order for the planning module404 to determine the data representing the trajectory 414, the planningmodule 404 receives data from the perception module 402, thelocalization module 408, and the database module 410.

The perception module 402 identifies nearby physical objects using oneor more sensors 121, e.g., as also shown in FIG. 1. The objects areclassified (e.g., grouped into types such as pedestrian, bicycle,automobile, traffic sign, etc.) and a scene description including theclassified objects 416 is provided to the planning module 404.

The planning module 404 also receives data representing the AV position418 from the localization module 408. The localization module 408determines the AV position by using data from the sensors 121 and datafrom the database module 410 (e.g., a geographic data) to calculate aposition. For example, the localization module 408 uses data from a GNSS(Global Navigation Satellite System) sensor and geographic data tocalculate a longitude and latitude of the AV. In an embodiment, dataused by the localization module 408 includes high-precision maps of theroadway geometric properties, maps describing road network connectivityproperties, maps describing roadway physical properties (such as trafficspeed, traffic volume, the number of vehicular and cyclist trafficlanes, lane width, lane traffic directions, or lane marker types andlocations, or combinations of them), and maps describing the spatiallocations of road features such as crosswalks, traffic signs or othertravel signals of various types. In an embodiment, the high-precisionmaps are constructed by adding data through automatic or manualannotation to low-precision maps.

The control module 406 receives the data representing the trajectory 414and the data representing the AV position 418 and operates the controlfunctions 420 a-c (e.g., steering, throttling, braking, ignition) of theAV in a manner that will cause the vehicle 100 to travel the trajectory414 to the destination 412. For example, if the trajectory 414 includesa left turn, the control module 406 will operate the control functions420 a-c in a manner such that the steering angle of the steeringfunction will cause the vehicle 100 to turn left and the throttling andbraking will cause the vehicle 100 to pause and wait for passingpedestrians or vehicles before the turn is made.

Autonomous Vehicle Inputs

FIG. 5 shows an example of inputs 502 a-d (e.g., sensors 121 shown inFIG. 1) and outputs 504 a-d (e.g., sensor data) that is used by theperception module 402 (FIG. 4). One input 502 a is a LiDAR (LightDetection and Ranging) system (e.g., LiDAR 123 shown in FIG. 1). LiDARis a technology that uses light (e.g., bursts of light such as infraredlight) to obtain data about physical objects in its line of sight. ALiDAR system produces LiDAR data as output 504 a. For example, LiDARdata is collections of 3D or 2D points (also known as a point clouds)that are used to construct a representation of the environment 190.

Another input 502 b is a RADAR system. RADAR is a technology that usesradio waves to obtain data about nearby physical objects. RADARs canobtain data about objects not within the line of sight of a LiDARsystem. A RADAR system produces RADAR data as output 504 b. For example,RADAR data are one or more radio frequency electromagnetic signals thatare used to construct a representation of the environment 190.

Another input 502 c is a camera system. A camera system uses one or morecameras (e.g., digital cameras using a light sensor such as acharge-coupled device [CCD]) to obtain information about nearby physicalobjects. A camera system produces camera data as output 504 c. Cameradata often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). In some examples, the camerasystem has multiple independent cameras, e.g., for the purpose ofstereopsis (stereo vision), which enables the camera system to perceivedepth. Although the objects perceived by the camera system are describedhere as “nearby,” this is relative to the AV. In some embodiments, thecamera system is configured to “see” objects far, e.g., up to akilometer or more ahead of the AV. Accordingly, in some embodiments, thecamera system has features such as sensors and lenses that are optimizedfor perceiving objects that are far away.

Another input 502 d is a traffic light detection (TLD) system. A TLDsystem uses one or more cameras to obtain information about trafficlights, street signs, and other physical objects that provide visualnavigation information. A TLD system produces TLD data as output 504 d.TLD data often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). A TLD system differs from a systemincorporating a camera in that a TLD system uses a camera with a widefield of view (e.g., using a wide-angle lens or a fish-eye lens) inorder to obtain information about as many physical objects providingvisual navigation information as possible, so that the vehicle 100 hasaccess to all relevant navigation information provided by these objects.For example, the viewing angle of the TLD system is about 120 degrees ormore.

In some embodiments, outputs 504 a-d are combined using a sensor fusiontechnique. Thus, either the individual outputs 504 a-d are provided toother systems of the vehicle 100 (e.g., provided to a planning module404 as shown in FIG. 4), or the combined output can be provided to theother systems, either in the form of a single combined output ormultiple combined outputs of the same type (e.g., using the samecombination technique or combining the same outputs or both) ordifferent types type (e.g., using different respective combinationtechniques or combining different respective outputs or both). In someembodiments, an early fusion technique is used. An early fusiontechnique is characterized by combining outputs before one or more dataprocessing steps are applied to the combined output. In someembodiments, a late fusion technique is used. A late fusion technique ischaracterized by combining outputs after one or more data processingsteps are applied to the individual outputs.

FIG. 6 shows an example of a LiDAR system 602 (e.g., the input 502 ashown in FIG. 5). The LiDAR system 602 emits light 604 a-c from a lightemitter 606 (e.g., a laser transmitter). Light emitted by a LiDAR systemis typically not in the visible spectrum; for example, infrared light isoften used. Some of the light 604 b emitted encounters a physical object608 (e.g., a vehicle) and reflects back to the LiDAR system 602. (Lightemitted from a LiDAR system typically does not penetrate physicalobjects, e.g., physical objects in solid form.) The LiDAR system 602also has one or more light detectors 610, which detect the reflectedlight. In an embodiment, one or more data processing systems associatedwith the LiDAR system generates an image 612 representing the field ofview 614 of the LiDAR system. The image 612 includes information thatrepresents the boundaries 616 of a physical object 608. In this way, theimage 612 is used to determine the boundaries 616 of one or morephysical objects near an AV.

FIG. 7 shows the LiDAR system 602 in operation. In the scenario shown inthis figure, the vehicle 100 receives both camera system output 504 c inthe form of an image 702 and LiDAR system output 504 a in the form ofLiDAR data points 704. In use, the data processing systems of thevehicle 100 compares the image 702 to the data points 704. Inparticular, a physical object 706 identified in the image 702 is alsoidentified among the data points 704. In this way, the vehicle 100perceives the boundaries of the physical object based on the contour anddensity of the data points 704.

FIG. 8 shows the operation of the LiDAR system 602 in additional detail.As described above, the vehicle 100 detects the boundary of a physicalobject based on characteristics of the data points detected by the LiDARsystem 602. As shown in FIG. 8, a flat object, such as the ground 802,will reflect light 804 a-d emitted from a LiDAR system 602 in aconsistent manner. Put another way, because the LiDAR system 602 emitslight using consistent spacing, the ground 802 will reflect light backto the LiDAR system 602 with the same consistent spacing. As the vehicle100 travels over the ground 802, the LiDAR system 602 will continue todetect light reflected by the next valid ground point 806 if nothing isobstructing the road. However, if an object 808 obstructs the road,light 804 e-f emitted by the LiDAR system 602 will be reflected frompoints 810 a-b in a manner inconsistent with the expected consistentmanner. From this information, the vehicle 100 can determine that theobject 808 is present.

Path Planning

FIG. 9 shows a block diagram 900 of the relationships between inputs andoutputs of a planning module 404 (e.g., as shown in FIG. 4). In general,the output of a planning module 404 is a route 902 from a start point904 (e.g., source location or initial location), and an end point 906(e.g., destination or final location). The route 902 is typicallydefined by one or more segments. For example, a segment is a distance tobe traveled over at least a portion of a street, road, highway,driveway, or other physical area appropriate for automobile travel. Insome examples, e.g., if the vehicle 100 is an off-road capable vehiclesuch as a four-wheel-drive (4WD) or all-wheel-drive (AWD) car, SUV,pick-up truck, or the like, the route 902 includes “off-road” segmentssuch as unpaved paths or open fields.

In addition to the route 902, a planning module also outputs lane-levelroute planning data 908. The lane-level route planning data 908 is usedto traverse segments of the route 902 based on conditions of the segmentat a particular time. For example, if the route 902 includes amulti-lane highway, the lane-level route planning data 908 includestrajectory planning data 910 that the vehicle 100 can use to choose alane among the multiple lanes, e.g., based on whether an exit isapproaching, whether one or more of the lanes have other vehicles, orother factors that vary over the course of a few minutes or less.Similarly, in some implementations, the lane-level route planning data908 includes speed constraints 912 specific to a segment of the route902. For example, if the segment includes pedestrians or un-expectedtraffic, the speed constraints 912 may limit the vehicle 100 to a travelspeed slower than an expected speed, e.g., a speed based on speed limitdata for the segment.

In an embodiment, the inputs to the planning module 404 includesdatabase data 914 (e.g., from the database module 410 shown in FIG. 4),current location data 916 (e.g., the AV position 418 shown in FIG. 4),destination data 918 (e.g., for the destination 412 shown in FIG. 4),and object data 920 (e.g., the classified objects 416 as perceived bythe perception module 402 as shown in FIG. 4). In some embodiments, thedatabase data 914 includes rules used in planning. Rules are specifiedusing a formal language, e.g., using Boolean logic. In any givensituation encountered by the vehicle 100, at least some of the ruleswill apply to the situation. A rule applies to a given situation if therule has conditions that are met based on information available to thevehicle 100, e.g., information about the surrounding environment. Rulescan have priority. For example, a rule that says, “if the road is afreeway, move to the leftmost lane” can have a lower priority than “ifthe exit is approaching within a mile, move to the rightmost lane.”

FIG. 10 shows a directed graph 1000 used in path planning, e.g., by theplanning module 404 (FIG. 4). In general, a directed graph 1000 like theone shown in FIG. 10 is used to determine a path between any start point1002 and end point 1004. In real-world terms, the distance separatingthe start point 1002 and end point 1004 may be relatively large (e.g.,in two different metropolitan areas) or may be relatively small (e.g.,two intersections abutting a city block or two lanes of a multi-laneroad).

In an embodiment, the directed graph 1000 has nodes 1006 a-drepresenting different locations between the start point 1002 and theend point 1004 that could be occupied by an vehicle 100. In someexamples, e.g., when the start point 1002 and end point 1004 representdifferent metropolitan areas, the nodes 1006 a-d represent segments ofroads. In some examples, e.g., when the start point 1002 and the endpoint 1004 represent different locations on the same road, the nodes1006 a-d represent different positions on that road. In this way, thedirected graph 1000 includes information at varying levels ofgranularity. In an embodiment, a directed graph having high granularityis also a subgraph of another directed graph having a larger scale. Forexample, a directed graph in which the start point 1002 and the endpoint 1004 are far away (e.g., many miles apart) has most of itsinformation at a low granularity and is based on stored data, but alsoincludes some high granularity information for the portion of the graphthat represents physical locations in the field of view of the vehicle100.

The nodes 1006 a-d are distinct from objects 1008 a-b which cannotoverlap with a node. In an embodiment, when granularity is low, theobjects 1008 a-b represent regions that cannot be traversed byautomobile, e.g., areas that have no streets or roads. When granularityis high, the objects 1008 a-b represent physical objects in the field ofview of the vehicle 100, e.g., other automobiles, pedestrians, or otherentities with which the vehicle 100 cannot share physical space. In anembodiment, some or all of the objects 1008 a-b are a static objects(e.g., an object that does not change position such as a street lamp orutility pole) or dynamic objects (e.g., an object that is capable ofchanging position such as a pedestrian or other car).

The nodes 1006 a-d are connected by edges 1010 a-c. If two nodes 1006a-b are connected by an edge 1010 a, it is possible for an vehicle 100to travel between one node 1006 a and the other node 1006 b, e.g.,without having to travel to an intermediate node before arriving at theother node 1006 b. (When we refer to an vehicle 100 traveling betweennodes, we mean that the vehicle 100 travels between the two physicalpositions represented by the respective nodes.) The edges 1010 a-c areoften bidirectional, in the sense that an vehicle 100 travels from afirst node to a second node, or from the second node to the first node.In an embodiment, edges 1010 a-c are unidirectional, in the sense thatan vehicle 100 can travel from a first node to a second node, howeverthe vehicle 100 cannot travel from the second node to the first node.Edges 1010 a-c are unidirectional when they represent, for example,one-way streets, individual lanes of a street, road, or highway, orother features that can only be traversed in one direction due to legalor physical constraints.

In an embodiment, the planning module 404 uses the directed graph 1000to identify a path 1012 made up of nodes and edges between the startpoint 1002 and end point 1004.

An edge 1010 a-c has an associated cost 1014 a-b. The cost 1014 a-b is avalue that represents the resources that will be expended if the vehicle100 chooses that edge. A typical resource is time. For example, if oneedge 1010 a represents a physical distance that is twice that as anotheredge 1010 b, then the associated cost 1014 a of the first edge 1010 amay be twice the associated cost 1014 b of the second edge 1010 b. Otherfactors that affect time include expected traffic, number ofintersections, speed limit, etc. Another typical resource is fueleconomy. Two edges 1010 a-b may represent the same physical distance,but one edge 1010 a may require more fuel than another edge 1010 b,e.g., because of road conditions, expected weather, etc.

When the planning module 404 identifies a path 1012 between the startpoint 1002 and end point 1004, the planning module 404 typically choosesa path optimized for cost, e.g., the path that has the least total costwhen the individual costs of the edges are added together.

Autonomous Vehicle Control

FIG. 11 shows a block diagram 1100 of the inputs and outputs of acontrol module 406 (e.g., as shown in FIG. 4). A control module operatesin accordance with a controller 1102 which includes, for example, one ormore processors (e.g., one or more computer processors such asmicroprocessors or microcontrollers or both) similar to processor 304,short-term and/or long-term data storage (e.g., memory random-accessmemory or flash memory or both) similar to main memory 306, ROM 308, andstorage device 310, and instructions stored in memory that carry outoperations of the controller 1102 when the instructions are executed(e.g., by the one or more processors).

In an embodiment, the controller 1102 receives data representing adesired output 1104. The desired output 1104 typically includes avelocity, e.g., a speed and a heading. The desired output 1104 can bebased on, for example, data received from a planning module 404 (e.g.,as shown in FIG. 4). In accordance with the desired output 1104, thecontroller 1102 produces data usable as a throttle input 1106 and asteering input 1108. The throttle input 1106 represents the magnitude inwhich to engage the throttle (e.g., acceleration control) of an vehicle100, e.g., by engaging the steering pedal, or engaging another throttlecontrol, to achieve the desired output 1104. In some examples, thethrottle input 1106 also includes data usable to engage the brake (e.g.,deceleration control) of the vehicle 100. The steering input 1108represents a steering angle, e.g., the angle at which the steeringcontrol (e.g., steering wheel, steering angle actuator, or otherfunctionality for controlling steering angle) of the AV should bepositioned to achieve the desired output 1104.

In an embodiment, the controller 1102 receives feedback that is used inadjusting the inputs provided to the throttle and steering. For example,if the vehicle 100 encounters a disturbance 1110, such as a hill, themeasured speed 1112 of the vehicle 100 is lowered below the desiredoutput speed. In an embodiment, any measured output 1114 is provided tothe controller 1102 so that the necessary adjustments are performed,e.g., based on the differential 1113 between the measured speed anddesired output. The measured output 1114 includes a measured position1116, a measured velocity 1118 (including speed and heading), a measuredacceleration 1120, and other outputs measurable by sensors of thevehicle 100.

In an embodiment, information about the disturbance 1110 is detected inadvance, e.g., by a sensor such as a camera or LiDAR sensor, andprovided to a predictive feedback module 1122. The predictive feedbackmodule 1122 then provides information to the controller 1102 that thecontroller 1102 can use to adjust accordingly. For example, if thesensors of the vehicle 100 detect (“see”) a hill, this information canbe used by the controller 1102 to prepare to engage the throttle at theappropriate time to avoid significant deceleration.

FIG. 12 shows a block diagram 1200 of the inputs, outputs, andcomponents of the controller 1102. The controller 1102 has a speedprofiler 1202 which affects the operation of a throttle/brake controller1204. For example, the speed profiler 1202 instructs the throttle/brakecontroller 1204 to engage acceleration or engage deceleration using thethrottle/brake 1206 depending on, e.g., feedback received by thecontroller 1102 and processed by the speed profiler 1202.

The controller 1102 also has a lateral tracking controller 1208 whichaffects the operation of a steering controller 1210. For example, thelateral tracking controller 1208 instructs the steering controller 1210to adjust the position of the steering angle actuator 1212 depending on,e.g., feedback received by the controller 1102 and processed by thelateral tracking controller 1208.

The controller 1102 receives several inputs used to determine how tocontrol the throttle/brake 1206 and steering angle actuator 1212. Aplanning module 404 provides information used by the controller 1102,for example, to choose a heading when the vehicle 100 begins operationand to determine which road segment to traverse when the vehicle 100reaches an intersection. A localization module 408 provides informationto the controller 1102 describing the current location of the vehicle100, for example, so that the controller 1102 can determine if thevehicle 100 is at a location expected based on the manner in which thethrottle/brake 1206 and steering angle actuator 1212 are beingcontrolled. In an embodiment, the controller 1102 receives informationfrom other inputs 1214, e.g., information received from databases,computer networks, etc.

Surface Guided Decision Making

FIG. 13A, FIG. 13B, and FIG. 13C show block diagrams of example systemsfor surface guided decision making. These systems are configured toclassify surfaces along a path of a vehicle (e.g., vehicle 100 shown inFIG. 1). These systems are also configured to control the vehicle basedon the classification of the surfaces. The components of the systems canbe located onboard the vehicle or remote from the vehicle. In someexamples, one or more components are implemented using a computer systemsimilar to the computer system 300 described in FIG. 3. Additionally oralternatively, one or more components can be implemented on a cloudcomputing environment similar to the cloud computing environment 200described in FIG. 2. Note that the systems are shown for illustrationpurposes only, as the systems can include additional components and/orhave one or more components removed without departing from the scope ofthe disclosure. Further, the various components of the systems can bearranged and connected in any manner. Although the following discussiondescribes the systems in the context of classifying one surface along avehicle path, the systems can simultaneously or consecutively classifymore than one surface along the vehicle path.

FIG. 13A shows an example system 1300 that is configured to use knownsurface information to classify a surface along a vehicle path. As shownin FIG. 13A, the system 1300 includes sensors 1302 (e.g., sensors thatare the same as, or similar to, sensors 121 of FIG. 1), surfaceclassifier 1304, motion planner 1306 (e.g., a motion planner that is thesame as, or similar to, planning module 404 described FIG. 4, FIG. 9,and FIG. 10), and controller 1308 (e.g., a controller that is the sameas, or similar to, control module 406 described in FIG. 4, FIG. 11, andFIG. 12).

In an embodiment, the sensors 1302 are configured to capture sensor dataassociated with a surface along a vehicle path. In some examples, thesensors 1302 and the captured sensor data are the same as, or similarto, the inputs 502 a-d and outputs 504 a-d of FIG. 5, respectively. Thepossible sensor data includes image data, location data (e.g., GPScoordinates, spatial location, or triangulation data), perception sensordata, weather data (e.g., temperature, humidity, precipitation), wheelrotation sensor data, IMU (e.g., gyroscope and/or accelerometer) data,geometric data (e.g., shape, elevation, dimensions, etc.), and pointclouds, among other examples. The surface can be along any portion ofthe vehicle path. The surface is, for example, along a portion of thevehicle path over which at least one wheel of the vehicle is scheduledto travel. In the example shown in FIG. 13A, the sensors 1302 areconfigured to send the sensor data to the surface classifier 1304.

In an embodiment, the surface classifier 1304 is configured to use knownsurface information to classify a surface based on the captured sensordata. The known surface information includes known surfaceclassifications and known properties (e.g., sensor measurements, rangesof sensor measurements, previously labeled data for road surfaceconditions, and/or the like that were previously generated based on avehicle moving over the known surface) of the known classifications. Inone example, the known surface information is used to train the surfaceclassifier 1304 to classify surfaces. The surface classifier 1304 can betrained using machine-learning algorithms, such as supervised learning.In supervised learning, inputs and corresponding outputs of interest areprovided to the surface classifier 1304. The surface classifier 1304adjusts its functions (e.g., in the case of a neural network, one ormore weights associated with two or more nodes of two or more differentlayers) based on a comparison of the output of the surface classifier1304 and an expected output in order to provide the desired output whensubsequent inputs are provided. Examples supervised learning algorithmsinclude deep neural networks, similarity learning, linear regression,random forests, k-nearest neighbors, support vector machines, anddecision trees.

In another example, the surface classifier 1304 classifies a surface bycomparing the sensor data to properties of known surfaceclassifications. In this example, if the surface classifier 1304identifies a threshold similarity between the sensor data and theproperties of a known surface classification, then the surfaceclassifier 1304 classifies the surface with that surface classification.The threshold similarity is a measure of similarity between the sensorreadings and the known properties that is greater than a predeterminedthreshold (e.g., the sensor readings and the known properties aregreater than 90% similar). For instance, a k-nearest neighbors algorithmcan be used to compare the sensor data to properties of known surfaceclassifications.

In another example, the surface classifier 1304 uses regression toquantify a road surface. In this example, after the surface classifier1304 classifies a surface as having a particular property, the surfaceclassifier 1304 can quantify an extent that the surface has thatproperty. For instance, a surface can be classified “icy,” and thenregression is used to estimate an extent of “iciness”, perhaps on ascale of 0-10. Alternatively, regression can be used to directlyestimate the coefficient of friction. Regression can be trained in asimilar manner as classification. For example, supervised learning canbe used. More specifically, the training data consists of road surfacesthat are labeled with ground truth properties (e.g., coefficient offriction, water depth, etc.). Example regression models include (deep)neural networks, linear regression, and support vector machines (SVMs).

In an embodiment, the surface classification is based on a surfacecomposition or a surface property. The surface composition is amanufactured material (e.g., asphalt, concrete, tar, bricks) or anaturally occurring element (e.g., rain, snow, sand, rocks). As such,the possible surface classifications include an asphalt surface, aconcrete surface, a tar surface, a brick surface, a rain surface, a snowsurface, a sand surface, a rock surface, among other examples. Thesurface property is a shape of the surface, whether the vehicle candrive over the surface (e.g., an obstacle), a coefficient of friction,or any property of the surface having a value with respect to athreshold. As such, a surface can be classified based on its shape,whether or not it is an obstacle, or whether the surface has a propertyvalue greater than, equal to, or less than a threshold. The surfaceclassification of temporary surfaces (e.g., a temporary natural element,such as snow or rain) includes a temporal description. For example, asnow surface is classified according to a length of time that it hasexisted (e.g., freshly packed snow, day old snow, etc.).

In some scenarios, the surface classifier 1304 determines that aclassification cannot be determined for the surface based on the knownsurface information (e.g., the surface classifier 1304 does not identifya known surface classification with similar properties to the surface).In these scenarios, the surface classifier 1304 classifies the surfaceas an unknown surface. As shown in FIG. 13A, the surface classifier 1304provides the surface classification to the motion planner 1306. In someexamples, the surface classifier 1304 also provides the motion planner1306 with the sensor data associated with the surface.

In an embodiment, the motion planner 1306 is configured to determine avehicle behavior based on the surface classification. In an example, themotion planner 1306 first determines drivability properties of thesurface based on the surface classification. Drivability properties caninclude physical characteristics that affect the manner in which avehicle drives over the surface. Example drivability properties includefriction, traction, road grip, resistance, rolling resistance,obstruction, among other properties. If the surface classification isknown to the system 1300, the motion planner 1306 obtains from adatabase of drivability properties associated with the known surfaceclassification. In some examples, the motion planner 1306 also generatesa surface map that includes a list of geometric descriptions of thesurface (e.g., generated based on the sensor data) and/or a distributionof the drivability properties of the surface.

In an embodiment, the motion planner 1306 determines the vehiclebehavior based on the drivability properties of the surface. In oneexample, the motion planner 1306 determines the vehicle behavior basedon known vehicle behaviors (e.g., historical vehicle behaviors). Morespecifically, the motion planner 1306 determines the vehicle behaviorbased on a known vehicle behavior associated with the known surfaceclassification or a surface with similar drivability properties. Examplevehicle behaviors include: following existing tracks (e.g., on a rainyor snowy surface), avoiding certain surfaces (e.g., avoiding icepatches), adjusting vehicle speed or torque, biasing within a lane,changing lanes, and defining a new center lane (e.g., to increasefriction on a road surface that is partially covered with snow or rain).In examples where the surface classification is unknown, the motionplanner 1306 determines a precautionary vehicle behavior (e.g., reducingspeed and avoiding the surface if possible). Once the motion planner1306 determines the vehicle behavior, the motion planner 1306 providesthe determined vehicle behavior to the controller 1308. The controller1308 then controls the vehicle based on the determined vehicle behavior.

FIG. 13B illustrates an example system 1310 that is configured to useknown surface information and feedback from a vehicle controller toclassify a surface along a vehicle path. As shown in FIG. 13B, like thesystem 1300, the system 1310 includes the sensors 1302, the motionplanner 1306, and the controller 1308. However, unlike the surfaceclassifier 1304, a surface classifier 1312 of the system 1310 receivesfeedback from the controller 1308.

In an embodiment, in addition to using known surface information toclassify surfaces, the surface classifier 1312 also uses feedback fromthe controller 1308. The feedback is used to generate new surfaceclassifications or to refine known surface classifications. The feedbackincludes sensor measurements captured when the vehicle was near asurface (e.g., within a threshold distance) or driving on the surface.In examples where the surface has a known classification, the surfaceclassifier 1312 uses the feedback to update the properties of theclassification (that is, update the output of the classification). Andin examples where the surface has an unknown classification, the surfaceclassifier 1312 uses the feedback to generate a new surfaceclassification. The surface classifier 1312 includes the feedback asproperties of the new surface classification. For example, the newsurface classification includes the feedback as labels used to identifythe new surface classification. The new surface classification and/orthe updated surface classification is used by the surface classifier1312 for classifying surfaces (e.g., using the techniques describedabove with respect to surface classifier 1304).

FIG. 13C illustrates an example system 1320 that is configured to useknown surface information, feedback from a vehicle controller, shareddata across a vehicle fleet, and data from external sources to classifya surface along a path of a vehicle. The system 1320 is also configuredto predict, based on the surface classification, a vehicle behavior ofone or more other vehicles near or driving on the surface. The system isalso configured to use the captured behavior of the other vehicles toestimate the road surface (e.g., a slipping vehicle can indicate aslippery surface) and to determine appropriate driving behaviors.Further, the system 1320 is configured to control a behavior of thevehicle based on the surface classification and/or the predictedbehavior of the one or more other vehicles. As shown in FIG. 13C, likethe systems 1300 of FIG. 13A and 1310 of FIG. 13B, the system 1320includes the sensors 1302, the motion planner 1306, and the controller1308. The system 1320 also includes surface classifier 1322, shareddynamic surface map 1324, external sources 1326, and motion predictor1328.

In an embodiment, in addition to capturing data associated with asurface along a vehicle path, the sensors 1302 are also configured tocapture sensor data indicative of the behavior of other vehicles thatare near the surface or driving on the surface. As shown in FIG. 13C,the captured behavior of other vehicles is provided to the motionpredictor 1328. As described below, the captured behavior of othervehicles is used to train the motion predictor 1328 to predict thebehavior of one or more other vehicles that are near or driving on asurface along a vehicle path.

In an embodiment, the shared dynamic surface map 1324 is a database(e.g., a database including a map) shared across a fleet of vehicles.The shared dynamic surface map 1324 receives information from thevehicles of the fleet and shares that information in the database. Forexample, the shared dynamic surface map 1324 receives from vehiclecontrollers, such as the vehicle controller 1308, surface propertyfeedback and vehicle behavior feedback. The surface property feedbackincludes sensor measurements captured when a vehicle was near a surfaceor driving on a surface. The vehicle behavior feedback includesinformation indicative of a vehicle trajectory and/or vehicle drivingsettings (e.g., speed or torque) when the vehicle was near a surface ordriving on a surface. In some examples, the shared dynamic surface map1324 receives information associated with a temporary surface (e.g., ametal plate that is temporarily placed over an opening in a roadsurface, a grated road surface, and/or the like). In such examples, theshared dynamic surface map 1324 schedules the information to expireafter a specified amount of time. The amount of time after which theinformation expires can be associated with (e.g., depend on) the type ofthe surface (e.g., information associated with a first temporary surface(e.g., a metal plate) can expire in an amount of days whereasinformation associated with second temporary surface (e.g., a gratedroad surface) can expire in an amount of days or an amount of weeks). Asshown in FIG. 13C, the surface classifier 1322 receives known surfaceinformation (e.g., known surface properties and surface classifications)from the shared dynamic surface map 1324. The known surface informationis used by the surface classifier 1322 to classify surfaces. Further,the motion predictor 1328 receives vehicle behavior feedback from theshared dynamic surface map 1324. The vehicle behavior feedback is usedby the motion predictor 1328 to predict the behavior of other vehicles.

In an embodiment, the external sources 1326 include databases thatprovide information associated with surfaces along a vehicle path. Forexample, the external sources 1326 include weather databases and/orconstruction databases that provide weather information and constructioninformation for areas along the vehicle path. As shown in FIG. 13C, thesurface classifier 1322 receives data from the external sources. Thesurface classifier 1322 uses the data to classify surfaces.

In an embodiment, the surface classifier 1322 is configured to receivesensor data from sensors 1302, feedback from the controller 1308, shareddata from the shared dynamic surface map 1324, and/or external data fromexternal sources 1326. In an example, the feedback from the controller1308, shared data from the shared dynamic surface map 1324, and/orexternal data from external sources 1326 is used to train the surfaceclassifier 1322 to classify surfaces (e.g., using the techniquesdescribed above with respect to surface classifier 1304 and surfaceclassifier 1312). For example, the data is used to generate new surfaceclassifications or to refine known surface classifications. The surfaceclassifications are used to classify a surface based on the sensor datareceived from the sensors 1302.

In an embodiment, the surface classifier 1322 receives sensor dataindicative of the behavior of another vehicle that is near the surfaceor driving on the surface. In this embodiment, the surface classifier1322 uses vehicle behavior to classify the surface on which the othervehicle is driving. For example, if the vehicle is slipping or sliding,the surface classifier 1322 determines that the surface is a slipperysurface. As described below, the surface classification can be used todetermine a vehicle behavior, e.g., determining a top speed based on thesurface classification. As shown in FIG. 13C, the surface classifier1322 provides the surface classification to the motion planner 1306 andthe motion predictor 1328.

In an embodiment, the motion predictor 1328 is configured to predict,based on a classification of a surface received from the surfaceclassifier 1322, the behavior of another vehicle that is near thesurface or driving on the surface. In one example, known vehiclebehavior, other vehicle behavior feedback received from the shareddynamic surface map 1324, and/or captured vehicle behavior of othervehicles received from the sensors 1302 is used to train the motionpredictor 1328 to predict the behavior of other vehicles. Morespecifically, the motion predictor 1328 can implement one or moremachine-learning algorithms, such as supervised learning andreinforcement learning. In such an example, the motion predictor 1328can be trained using the known vehicle behaviors, the other vehiclebehavior feedback, and/or the captured vehicle behavior of othervehicles. In another example, the motion predictor 1328 predicts thebehavior of another vehicle by comparing how an observed vehicle behaveswith how vehicles have historically behaved when near the surface ordriving on the surface. As shown in FIG. 13C, the motion predictor 1328provides the predicted vehicle behavior to the motion planner 1306.

In an embodiment, the motion planner 1306 is configured to determine avehicle behavior based on the surface classification received from thesurface classifier 1322 and/or the predicted vehicle behavior receivedfrom the motion predictor 1328. More specifically, the motion planner1306 determines the vehicle behavior based on the surface classificationdrivability properties of the surface. The motion planner 1306 thendetermines the vehicle behavior based on the drivability properties ofthe surface and/or the predicted vehicle behavior of another vehiclethat is near the surface or driving on the surface. As an example, themotion planner 1306 determines a vehicle behavior that causes thevehicle to drive in the track of other vehicles while it is snowing(e.g., to maximize the friction and minimize the risk of losingcontrol). As another example, the motion planner 1306 determines thevehicle behavior based on historical vehicle behaviors on surfaces withthe same or similar drivability properties. As yet another example, themotion planner 1306 determines a vehicle behavior that follows or avoidsthe other vehicle that is reacting to the surface. In some examples, themotion planner 1306 also determines a vehicle behavior that isassociated with a safety or performance value that is greater than acurrent safety or performance value associated with a current vehiclebehavior. The motion planner 1306 provides the controller 1308 with thevehicle motion.

In an embodiment, the controller 1308 then controls the vehicle based onthe determined vehicle behavior. As shown in FIG. 13C, the controller1308 also sends feedback to the surface classifier 1322. Further, thecontroller 1308 sends surface property feedback and/or vehicle behaviorfeedback to the shared dynamic surface map 1324. In some examples, thecontroller 1308 sends vehicle behavior feedback to the motion planner1306.

FIG. 14 shows a flowchart of a process 1400 for surface guided decisionmaking. For example, the process could be carried out by the system 1300of FIG. 13A, the system 1310 of FIG. 13B, or the system 1320 of FIG.13C. Sensor data associated with a surface (e.g., image of the surface,scan of the surface, or a location of the surface) along a path to betraveled by a vehicle is received 1402 from at least one sensor (e.g., acamera, LiDAR [described in FIG. 6, FIG. 7, and FIG. 8], radar, or alocation sensor) of the vehicle.

A surface classifier is used to determine 1404 a classification of thesurface (e.g., type of surface or material on the surface, such as snow,ice, sand, chemicals [e.g., oil or paint], pebbles, rock, dust, or anyother material that changes the drivability of the surface) based on thesensor data. Drivability properties of the surface (e.g., such asfriction, traction, road grip, resistance, rolling resistance,obstruction) are determined 1406 based on the classification of thesurface.

A behavior of the vehicle when driving near the surface or on thesurface is planned based on the drivability properties of the surface at1408. Examples of the behavior include determining a motion thataccounts for the drivability properties, determining the motion based onprevious vehicle motion (either the vehicle or another vehicle) onsurfaces with the same or similar drivability properties, determiningthe motion based on the predicted motion of another vehicle driving onthe surface, following existing tracks on the road when it is snowing orraining, avoiding ice patches, reducing speed, biasing within a lane,changing lanes to avoid an obstacle, defining a new center lane(baseline) path to increase friction on road surface that is partiallycovered with snow, reduce speed/torque over compromised road segments,follow or avoid another vehicle that is reacting to the surface. Thevehicle is controlled based on the behavior of the vehicle at 1410.

In some implementations, determining, based on the surfaceclassification, drivability properties of the surface involvesgenerating a surface map that includes at least one of: a list ofgeometric descriptions of the surface or a distribution of thedrivability properties on the path of the vehicle.

In some implementations, the surface classification includes a knownsurface, and determining, based on the surface classification,drivability properties of the surface involves obtaining, from adatabase, drivability properties associated with the known surface.

In some implementations, the surface classification is an unknownsurface, and determining, based on the surface classification,drivability properties of the surface involves: determining, from adatabase, sensor measurements included in a label of the unknownsurface, wherein the sensor measurements are historical sensormeasurements associated with the unknown surface; and determining thedrivability properties of the unknown surface based on the sensormeasurements.

In some implementations, the historical sensor measurements are measuredby the vehicle or received from another vehicle.

In some implementations, planning, based on the drivability propertiesof the surface, a behavior of the vehicle when driving near the surfaceor on the surface involves determining, based on the drivabilityproperties, a vehicle motion that is associated with a safety orperformance value that is greater than a current safety or performancevalue associated with a current vehicle motion.

In some implementations, the surface is a first surface, and planning,based on the drivability properties of the surface, a behavior of thevehicle when driving near the surface or on the surface involvesdetermining a historical vehicle motion performed on a second surfacethat has properties similar to the drivability properties of the firstsurface.

In some implementations, the vehicle is a first vehicle, and planning,based on the drivability properties of the surface, a behavior of thevehicle when driving near the surface or on the surface involvesdetecting a second vehicle in proximity of the first vehicle;determining, based on the drivability properties of the surface, anexpected motion of the second vehicle; and determining, based on theexpected motion of the second vehicle, the behavior of the firstvehicle.

In some implementations, the surface classifier receives, from the atleast one sensor, sensor measurements performed when the vehicle drivesover the surface.

In some implementations, the surface classification is a known surfaceclassification, and the process 1400 further involves updating, based onthe sensor measurements, a classifier associated with the surfaceclassification.

In some implementations, the surface classification is an unknownsurface, and wherein the process 1400 further involves adding the sensormeasurements to a label associated with the unknown surface.

In some implementations, the process 1400 further involves receivingfrom a shared dynamic database at least one of a road surfaceclassification information or known surface property information.

In some implementations, the vehicle is a first vehicle, and the method1400 further involves capturing, using the at least one sensor, a motionof a second vehicle that is driving on the surface.

In some implementations, the process 1400 further involves sending to ashared dynamic database at least one of: surface property feedback orvehicle motion feedback when the vehicle drives on the surface.

In the foregoing description, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The description and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction. Any definitions expressly set forthherein for terms contained in such claims shall govern the meaning ofsuch terms as used in the claims. In addition, when we use the term“further comprising”, in the foregoing description or following claims,what follows this phrase can be an additional step or entity, or asub-step/sub-entity of a previously-recited step or entity.

What is claimed is:
 1. A system, comprising: at least one sensor; atleast one computer-readable medium storing computer-executableinstructions; at least one processor configured to communicate with theat least one sensor and to execute the computer executable instructions,the execution carrying out operations including: receiving, from the atleast one sensor, sensor data associated with a surface along a path tobe traveled by a vehicle; using a surface classifier to determine aclassification of the surface based on the sensor data; determining,based on the classification of the surface, drivability properties ofthe surface; planning, based on the drivability properties of thesurface, a behavior of the vehicle when driving near the surface or onthe surface; and controlling the vehicle based on the planned behavior.2. The system of claim 1, wherein determining, based on the surfaceclassification, drivability properties of the surface comprises:generating a surface map that includes at least one of: a list ofgeometric descriptions of the surface or a distribution of thedrivability properties on the path of the vehicle.
 3. The system ofclaim 1, wherein the surface classification includes a known surface,and wherein determining, based on the surface classification,drivability properties of the surface comprises: obtaining, from adatabase, the drivability properties associated with the known surface.4. The system of claim 1, wherein the surface classification is anunknown surface, and wherein determining, based on the surfaceclassification, the drivability properties of the surface comprises:determining, from a database, sensor measurements included in a label ofthe unknown surface, wherein the sensor measurements are historicalsensor measurements associated with the unknown surface; and determiningthe drivability properties of the unknown surface based on the sensormeasurements.
 5. The system of claim 4, wherein the historical sensormeasurements are measured by the vehicle or received from anothervehicle.
 6. The system of claim 1, wherein planning, based on thedrivability properties of the surface, a behavior of the vehicle whendriving near the surface or on the surface comprises: determining, basedon the drivability properties, a vehicle motion that is associated witha safety or performance value that is greater than a current safety orperformance value associated with a current vehicle motion.
 7. Thesystem of claim 1, wherein the surface is a first surface, and whereinplanning, based on the drivability properties of the surface, a behaviorof the vehicle when driving near the surface or on the surfacecomprises: determining a historical vehicle motion performed on a secondsurface that has properties similar to the drivability properties of thefirst surface.
 8. The system of claim 1, wherein the vehicle is a firstvehicle, and wherein planning, based on the drivability properties ofthe surface, a behavior of the vehicle when driving near the surface oron the surface comprises: detecting a second vehicle in proximity of thefirst vehicle; determining, based on the drivability properties of thesurface, an expected motion of the second vehicle; and determining,based on the expected motion of the second vehicle, the behavior of thefirst vehicle.
 9. The system of claim 1, wherein the surface classifierreceives, from the at least one sensor, sensor measurements performedwhen the vehicle drives over the surface.
 10. The system of claim 8,wherein the surface classification is a known surface classification,and wherein the operations further comprise: updating, based on thesensor measurements, a classifier associated with the surfaceclassification.
 11. The system of claim 8, wherein the surfaceclassification is an unknown surface, and wherein the operations furthercomprise: adding the sensor measurements to a label associated with theunknown surface.
 12. The system of claim 1, the operations furthercomprising: receiving from a shared dynamic database at least one of aroad surface classification information or known surface propertyinformation.
 13. The system of claim 1, wherein the vehicle is a firstvehicle, and wherein the operations further comprise: capturing, usingthe at least one sensor, a motion of a second vehicle that is driving onthe surface.
 14. The system of claim 1, the operations furthercomprising: sending to a shared dynamic database at least one of:surface property feedback or vehicle motion feedback when the vehicledrives on the surface.
 15. A method comprising: receiving, from at leastone sensor of a vehicle, sensor data associated with a surface along apath to be traveled by a vehicle; using a surface classifier todetermine a classification of the surface based on the sensor data;determining, based on the classification of the surface, drivabilityproperties of the surface; planning, based on the drivability propertiesof the surface, a behavior of the vehicle when driving near the surfaceor on the surface; and controlling the vehicle based on the plannedbehavior.
 16. A non-transitory computer-readable storage mediumcomprising at least one program for execution by at least one processorof a first device, the at least one program including instructionswhich, when executed by the at least one processor, cause the firstdevice to perform the method of claim 15.